The SARS-COV-2 pandemic has been a global pub -lic health problem since 2019, with over 400 million reported cases, 6 million deaths, and significant economic and social damage. Overlapping SARS-CoV-2 infection in patients with chronic diseases, such as multiple sclerosis (MS), causes manage-ment problems, especially in patients treated with disease-modifying therapies. Studies investigating COVID-19 vaccination effectiveness have shown vari-ability in postvaccination immune response that depends on the patient's background treatment, and special attention is required for anti-CD20 therapies.Existing data on the efficacy of COVID-19 vaccination in patients with MS undergoing disease-modifying treatment are summarized and critically evaluated in this article.

Integrated pulmonary rehabilitation (PR) is a fundamental pillar in the modern management of chronic lung diseases, aimed at significantly improving the quality of life and overall functionality of patients. According to the latest 2023 American Thoracic Society (ATS) guidelines, PR is defined as a multidimensional set of interventions designed to enhance the physical and psychosocial well-being of patients with chronic respiratory diseases through a personalized approach [1]. This rehabilitative model includes a broad spectrum of measures intended to reduce respiratory symptoms, increase exercise tolerance, and enhance patient autonomy within the context of a multidisciplinary team comprising pulmonologists, physiotherapists, nutritionists, psychologists, and social workers [2]. The origins of PR can be traced back to the 1960s, when physical training was the primary therapeutic strategy for patients with respiratory conditions. Over the decades, as the understanding of chronic lung diseases deepened, this concept evolved substantially, adding essential components such as psychological counseling, nutritional support, education, and social integration, all of which are now standard in treatment protocols [3]. The 2023 ATS guidelines emphasize the necessity of a holistic approach and underscore the central role of personalized rehabilitation programs to meet patients’ individual needs [4]. Physical training, focused on respiratory muscle rehabilitation and general endurance, remains the core of any PR program, with solid evidence supporting improvements in exercise capacity and reduction of dyspnea [5]. Patient education is also central, emphasizing symptom self-management, treatment adherence, and modification of risk behaviors [6]. Psychological support is crucial, given the high prevalence of depression and anxiety among patients with chronic lung diseases, while nutritional counseling contributes to maintaining homeostasis and muscle mass, essential for optimal pulmonary function [7]. Clinical studies highlight the positive outcomes of integrated PR on clinical and functional parameters, including significant reductions in dyspnea, increased exercise capacity, improved quality of life, and decreased hospitalization rates [2]. These benefits are also associated with a reduction in mortality rates, providing conclusive evidence for the importance of a multidisciplinary approach and personalized interventions [3]. In conclusion, integrated PR represents a comprehensive approach essential for optimizing the treatment of patients with chronic lung diseases, with a significant impact on clinical outcomes and quality of life.

Current diagnostic assessments of lung disease rely primarily on medical imaging and global pulmonary function tests, which either provide static structural information or collapse complex regional behavior into global indices. As a result, important information about regional mechanical heterogeneity and early pathological changes may remain inaccessible. In this work, we introduce a conceptual diagnostic framework for the lung based on mechanical system identification and evaluate its feasibility using simulation-based analysis. Rather than directly imaging internal lung structure, the lung–thorax system is treated as an identifiable viscoelastic dynamical system whose internal mechanical properties can be inferred from its response to controlled external excitation. A multi-degree-of-freedom mechanical representation of the lung was developed to capture the dominant low-frequency behavior of the chest wall and major lung regions. Sensitivity and Fisher-information analysis confirmed the structural identifiability of regional stiffness parameters (FIM eigenvalues λ₁ = 1.75 × 10⁻⁹ and λ₂ = 8.91 × 10⁻¹⁰). Inverse fitting experiments accurately recovered simulated stiffness perturbations (e.g., k₀₁ = 240 → 239.5; k₀₂ = 154 → 159.5) from noisy frequency response data, while classification experiments achieved the complete separation of simulated pathological configurations in an idealized synthetic scenario, supporting theoretical discriminability rather than clinical performance claims. These findings demonstrate the theoretical feasibility of a diagnostic paradigm in which regional lung mechanical alterations can in principle be identified through mechanical system identification rather than direct imaging, thereby suggesting a complementary approach for a non-invasive assessment of regional lung mechanics from externally measured responses. By quantifying regional stiffness and mechanical heterogeneity, this framework may also support the personalization and monitoring of pulmonary rehabilitation strategies in chronic respiratory disease.

Background: Pulmonary fibrosis (PF) and post-infectious fibrotic lung disease are characterized by marked heterogeneity in radiologic patterns, physiologic impairment, and clinical presentation. Conventional analytic approaches often fail to capture non-linear and multimodal relationships between structural imaging findings and functional limitation. Integrating imaging-derived representations with clinical and functional data using artificial intelligence (AI) may provide a more comprehensive characterization of disease heterogeneity. Objectives: The objective of this study was to develop and evaluate a multimodal AI framework combining imaging-derived embeddings and structured clinical data to identify atypical clinical–radiologic profiles in patients with pulmonary fibrosis using unsupervised anomaly detection. Methods: A retrospective cohort of 41 patients with radiologically confirmed pulmonary fibrosis or post-infectious fibrotic lung disease was analyzed. Deep imaging embeddings were extracted from baseline thoracic CT examinations using a pretrained convolutional neural network and integrated with standardized clinical and functional variables. A multimodal variational autoencoder (VAE) was trained in an unsupervised manner to learn the distribution of typical patient profiles. Patient-specific anomaly scores were derived from reconstruction error plus latent regularization (β·KL divergence). Associations between anomaly scores, disease severity, and clinical markers were assessed using Spearman rank correlation. Results: Anomaly scores were right-skewed (median 26.91, IQR 22.87–32.11; range 19.75–46.18). Patients above the 85th percentile (anomaly score ≥ 33.85) comprised 7/41 (17.1%) of the cohort and occurred across all clinician-assigned severity categories (mild 3, moderate 1, severe 3). Anomaly scores overlapped substantially across severity groups, with similar medians (mild 26.47, moderate 28.55, severe 28.23). Correlations with conventional severity markers were weak and non-significant, including DLCO (% predicted; ρ = −0.25, p = 0.115) and FEV₁ (% predicted; ρ = −0.22, p = 0.165), a pattern consistent with anomaly scores reflecting multimodal deviation rather than severity alone, while acknowledging the exploratory nature of the analysis. Highly anomalous patients frequently exhibited discordant clinical–radiologic profiles, including preserved functional capacity despite marked imaging-derived deviation or disproportionate physiological impairment relative to imaging patterns. Conclusions: This proof-of-concept study demonstrates that multimodal VAE-based anomaly detection integrating imaging-derived embeddings with clinical data can quantify clinical–radiologic heterogeneity in pulmonary fibrosis beyond conventional severity stratification. Unsupervised anomaly detection provides a complementary framework for identifying atypical multimodal profiles and supporting individualized phenotyping and hypothesis generation in fibrotic lung disease. Given the modest cohort size, these findings should be interpreted as illustrative and hypothesis-generating rather than generalizable.

Introduction: Monitoring variables of COVID-19/ Post-Covid Infection (PCI) and comorbidities is essential in severe disease forms. Aim: To identify biological variables that predict severe forms of COVID-19/PCI in patients with comorbidities like COPD, asthma, lung cancer. Material and Methods: Data of 182 Covid-19/PCI patients included complete blood count (CBC), Erythrocyte Sedimentation Rate (ESR), neutrophyl lymphocyte ratio (NLR), glucose, C-reactive protein (CRP), Lactat dehidrogenase (LDH). Differences in laboratory biomarkers as by COVID-19/PCI severity were tested with The Mann-Whitney U test. Receiver operating curve (ROC) and area under the curve (AUC) were calculated to compare the diagnostic performance of each parameter. Results: The lymphocyte and neutrophil count, CRP, LDH, age and hospitalization duration differed statistically significantly (p < 0.005; p < 0.0005) in all groups. The diagnostic accuracy was supported by the biomarkers specificity and sensitivity by ROC curves (Figure 1). Thus, NLR, CRP, LDH and glucose were considered important predictors in evaluating patients with COVID-19 (AUC > 0.7). erj;64/suppl_68/PA3999/F1 F1 F1 Conclusions: Modified inflammatory markers in COVID-19/PCI and high values of the average concentrations of serum CRP and LDH were observed especially in asthma and COPD patients. Long COVID-19 in COPD led to exacerbated respiratory symptoms and increased risk of severe complications.

Background: Post-COVID-19 syndrome represents a global health problem, manifesting with persistent multiple organ symptoms, including respiratory ones. This review aimed to evaluate the efficacy of different pulmonary rehabilitation interventions in the management of the disease, including its particularities. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement and prospectively registered in the PROSPERO database (CRD420261292824). PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched from January 2021 to January 2026. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the Cochrane Risk of Bias 2 (RoB 2) tool. Twenty randomized controlled trials involving 2101 adult patients with post-COVID-19 syndrome met the eligibility criteria and were included in the qualitative synthesis. Results: Pulmonary rehabilitation (PR) demonstrated significant improvements in exercise capacity, with notable increases in 6MWT and VO₂max. Respiratory and peripheral muscle strength significantly increased. The quality of life, dyspnea and fatigue consistently improved. The results for intrinsic pulmonary function, anxiety, and depression were heterogeneous, suggesting benefits more related to reconditioning. Conclusions: Pulmonary rehabilitation is an essential and efficient component in post-COVID-19 syndrome, improving physical function, respiratory and peripheral muscle strength, the quality of life, fatigue and dyspnea. Telerehabilitation is a viable alternative. Adapting protocols is crucial, recognizing that most benefits derive from physical reconditioning and muscle training, not necessarily from major structural pulmonary changes.